Sample summary
Input data: infant stools (BS1, BS2, BS3, and BS5) and maternal stool (MS1)
- n samples = 2086
- n taxa: genera = 502; variants = 6372
- n taxa (infant): genera = 502; variants = 6372
- n taxa (rarefied): genera = 461; variants = 4596
- mean read count = 1.4312310^{5}, s.d. = 1.3611310^{5}
Statistical methods for mixed-effects models
- Linear mixed-effects models fit with family ID as a random effect
- Effect of country determined by comparing: (i) model with country/age vs (ii) model with age alone
- Pairwise comparisons between countries (M vs I, U vs I, U vs M) corrected by FDR to control family error rate
- OPV/IPV arms from India combined in primary comparison
- Diversity in maternal samples compared by country via ANOVA with post-hoc Tukey test
Taxon abundance profile
Column
Genus-level
India - infant
N: 1147
Malawi - infant
N: 285
UK - infant
N: 226
India - maternal
Malawi - maternal
UK - maternal
RSV-level
India - infant
Malawi - infant
UK - infant
India - maternal
Malawi - maternal
UK - maternal
Alpha diversity
Column
Genus-level
India vs Malawi vs UK - Shannon
India vs Malawi vs UK - richness
India vs Malawi vs UK with cross-sectional p values - Shannon
India vs Malawi vs UK with cross-sectional p values - richness
N by timepoint
IND MLW UK
BS1 289 81 59
BS2 287 75 59
BS3 289 68 53
BS5 282 61 55p values, longitudinal mixed-effects models (FDR-adjusted, 4 decimal places)
Shannon Richness
MLW-IND 0.0000 0.0000
UK-IND 0.2262 0.4374
UK-MLW 0.0300 0.0000p values, India (OPV) vs India (IPV)
Shannon Richness
India (OPV)-India (IPV) 0.4652 0.3768p values, cross-sectional models
week of life 1
Shannon Richness
MLW-IND 0.0001 0.0000
UK-IND 0.4844 0.6809
UK-MLW 0.0002 0.0000week of life 4
Shannon Richness
MLW-IND 0.0002 0.0000
UK-IND 0.0661 0.1164
UK-MLW 0.4646 0.0488week of life 6
Shannon Richness
MLW-IND 0.2354 0.0000
UK-IND 0.2310 0.0503
UK-MLW 0.9894 0.2179week of life 10
Shannon Richness
MLW-IND 0.1013 0.0001
UK-IND 0.2347 0.4217
UK-MLW 0.9614 0.0690Maternal samples - Shannon
Maternal samples - richness
p values, India vs Malawi vs UK
Shannon Richness
MLW-IND 0.9997 0.0625
UK-IND 0.0002 0.0002
UK-MLW 0.0026 0.0000p values, India (OPV) vs India (IPV)
Shannon Richness
1 0.6225 0.7129Longitudinal abundance plots
Infants - India vs UK vs Malawi
N by timepoint
India Malawi UK
BS1 289 81 59
BS2 287 75 59
BS3 289 68 53
BS5 282 61 55
MS1 288 83 57Infants - India (OPV) vs India (IPV)
Maternal samples (with genera corresponding to infant plots included)
Beta diversity
Column
PERMANOVA - unweighted
Inter-individual differences among all infant samples
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
family_ID 473 146.38 0.309479 3.4394 0.57877 0.001 ***
Residuals 1184 106.54 0.089982 0.42123
Total 1657 252.92 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Differences by country stratified by age
week R2 p n
1 week of life 1 0.08963047 0.001 429
2 week of life 4 0.07156166 0.001 421
3 week of life 6 0.06425047 0.001 410
4 week of life 10 0.06603234 0.001 398
5 mother 0.16414628 0.001 428Summary plot
PERMANOVA - weighted
Inter-individual differences among all infant samples
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
family_ID 473 204.66 0.43268 2.895 0.5363 0.001 ***
Residuals 1184 176.96 0.14946 0.4637
Total 1657 381.61 1.0000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Differences by country stratified by age
week R2 p n
1 week of life 1 0.09855442 0.001 429
2 week of life 4 0.10665922 0.001 421
3 week of life 6 0.08819497 0.001 410
4 week of life 10 0.11127149 0.001 398
5 mother 0.17832746 0.001 428Summary plot
Turnover, paired vs random (weighted)
For week 1, distance from maternal sample plotted. For subsequent samples, distance from prior infant sample plotted.
Turnover, paired vs random (unweighted)
Longitudinal samples from a given infant more closely clustered than expected by chance in each of the three cohorts.
Differential taxa
Column
Genus-level
Cross-validation accuracy, India vs Malawi vs UK
Pre-vaccination comparisons
Differential abundance plots - significant by Fisher’s test or Aldex2 (FDR p <0.05)
Longitudinal abundance comparisons
Column
Genus-level
India vs UK
Number of genera tested: 31
Number of discriminant genera (FDR p < 0.05): 20 (India = 10; UK = 10) Failed to converge: 0
India vs Malawi
Number of genera tested: 43
Number of discriminant genera (FDR p < 0.05): 22 (India = 6; Malawi = 16) Failed to converge: 0
Malawi vs UK
Number of genera tested: 45
Number of discriminant genera (FDR p < 0.05): 29 (Malawi = 22; UK = 7) Failed to converge: 0
Construct tree
Longitudinal abundance plots for major genera
Session info
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] FSA_0.9.0 ALDEx2_1.18.0
[3] sjstats_0.18.0 ggExtra_0.9
[5] formattable_0.2.0.1 NBZIMM_1.0
[7] inlmisc_0.5.2 decontam_1.6.0
[9] ggtree_2.0.4 wesanderson_0.3.6
[11] phangorn_2.5.5 ape_5.4-1
[13] DECIPHER_2.14.0 RSQLite_2.2.1
[15] Biostrings_2.54.0 XVector_0.26.0
[17] cowplot_1.1.0 scales_1.1.1
[19] RVAideMemoire_0.9-78 DescTools_0.99.38
[21] ggsignif_0.6.0 binom_1.1-1
[23] shiny_1.5.0 randomcoloR_1.1.0.1
[25] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[27] DelayedArray_0.12.3 BiocParallel_1.20.1
[29] Biobase_2.46.0 GenomicRanges_1.38.0
[31] GenomeInfoDb_1.22.1 IRanges_2.20.2
[33] S4Vectors_0.24.4 BiocGenerics_0.32.0
[35] crossval_1.0.3 UpSetR_1.4.0
[37] labdsv_2.0-1 mgcv_1.8-33
[39] nlme_3.1-149 ggpubr_0.4.0
[41] data.table_1.12.8 corrplot_0.84
[43] ZIBR_0.1 vegan_2.5-6
[45] lattice_0.20-41 permute_0.9-5
[47] randomForest_4.6-14 matrixStats_0.57.0
[49] lme4_1.1-23 Matrix_1.2-18
[51] reshape2_1.4.4 pheatmap_1.0.12
[53] DT_0.16 plotly_4.9.3
[55] cluster_2.1.0 tidyr_1.1.2
[57] dplyr_1.0.2 magrittr_1.5
[59] plyr_1.8.6 kableExtra_1.2.1
[61] gridExtra_2.3 RColorBrewer_1.1-2
[63] knitr_1.30 ggplot2_3.3.2
[65] phyloseq_1.30.0
loaded via a namespace (and not attached):
[1] estimability_1.3 coda_0.19-4 bit64_4.0.5
[4] rpart_4.1-15 RCurl_1.98-1.2 generics_0.0.2
[7] bit_4.0.4 webshot_0.5.2 xml2_1.3.2
[10] httpuv_1.5.4 xfun_0.18 hms_0.5.3
[13] evaluate_0.14 promises_1.1.1 readxl_1.3.1
[16] igraph_1.2.6 DBI_1.1.0 geneplotter_1.64.0
[19] htmlwidgets_1.5.3 purrr_0.3.4 ellipsis_0.3.1
[22] backports_1.1.10 V8_3.2.0 insight_0.9.6
[25] annotate_1.64.0 vctrs_0.3.4 sjlabelled_1.1.7
[28] abind_1.4-5 withr_2.3.0 checkmate_2.0.0
[31] rgdal_1.5-18 emmeans_1.5.1 treeio_1.10.0
[34] lazyeval_0.2.2 crayon_1.3.4 flexdashboard_0.5.2
[37] genefilter_1.68.0 labeling_0.3 pkgconfig_2.0.3
[40] nnet_7.3-14 rlang_0.4.8 lifecycle_0.2.0
[43] miniUI_0.1.1.1 modelr_0.1.8 cellranger_1.1.0
[46] raster_3.3-13 carData_3.0-4 Rhdf5lib_1.8.0
[49] boot_1.3-25 base64enc_0.1-3 png_0.1-7
[52] viridisLite_0.3.0 parameters_0.8.6 rootSolve_1.8.2.1
[55] bitops_1.0-6 blob_1.2.1 stringr_1.4.0
[58] jpeg_0.1-8.1 rstatix_0.6.0 memoise_1.1.0
[61] zlibbioc_1.32.0 compiler_3.6.1 ade4_1.7-15
[64] htmlTable_2.1.0 Formula_1.2-4 MASS_7.3-53
[67] tidyselect_1.1.0 stringi_1.5.3 forcats_0.5.0
[70] yaml_2.2.1 locfit_1.5-9.4 latticeExtra_0.6-29
[73] grid_3.6.1 fastmatch_1.1-0 tools_3.6.1
[76] lmom_2.8 rio_0.5.16 rstudioapi_0.11
[79] foreach_1.5.1 foreign_0.8-71 gld_2.6.2
[82] farver_2.0.3 Rtsne_0.15 digest_0.6.25
[85] rvcheck_0.1.8 BiocManager_1.30.10 quadprog_1.5-8
[88] Rcpp_1.0.5 car_3.0-10 broom_0.7.1
[91] performance_0.5.0 later_1.1.0.1 httr_1.4.2
[94] AnnotationDbi_1.48.0 effectsize_0.3.3 colorspace_1.4-1
[97] rvest_0.3.6 XML_3.99-0.3 splines_3.6.1
[100] statmod_1.4.34 tidytree_0.3.3 expm_0.999-5
[103] sp_1.4-4 multtest_2.42.0 Exact_2.1
[106] xtable_1.8-4 jsonlite_1.7.1 nloptr_1.2.2.2
[109] R6_2.4.1 Hmisc_4.4-1 pillar_1.4.6
[112] htmltools_0.5.0 mime_0.9 glue_1.4.2
[115] fastmap_1.0.1 minqa_1.2.4 class_7.3-17
[118] codetools_0.2-17 mvtnorm_1.1-1 tibble_3.0.4
[121] curl_4.3 zip_2.1.1 openxlsx_4.2.2
[124] survival_3.2-7 rmarkdown_2.4 biomformat_1.14.0
[127] munsell_0.5.0 e1071_1.7-4 rhdf5_2.30.1
[130] GenomeInfoDbData_1.2.2 iterators_1.0.13 sjmisc_2.8.5
[133] haven_2.3.1 gtable_0.3.0 bayestestR_0.7.2 .